In this paper, we investigate whether firms' engagements in collaboration agreements with different types of external stakeholders produce complementary effects on the likelihood of eco-innovation. Although collaboration network and open eco-innovation theories affirm that the combination of external partners such as scientific partners, suppliers and customers produces complementary effects on the firm's likelihood of eco-innovation, several empirical studies found the existence of substitutive effects between them. To bridge this gap in the literature, we shape the nature of the interaction between different external partners, analysing an unbalanced panel sample of 10,918 innovative Spanish firms, covering the period 2008-2016. Consequently, we can show how firms benefit the most from collaboration with external partners. Our results show that firms that simultaneously collaborate with scientific partners, suppliers and customers generate partial complementary effects, which increase the firm's likelihood to eco-innovate the most, and that the combination of customer-collaboration with scientific partners, or suppliercollaboration, produces partial substitutive effects. Taking this in account, our results also confirm that engaging with scientific partners, suppliers or customers, independent of one another, increases firms' likelihood of eco-innovation more than noncollaboration. These results have important implications for managers, researchers and policy designers. For managers, this study provides a correct understanding of the benefits that they can expect to obtain from multi-partner external collaboration. For researchers, it introduces the marginal analysis to estimate interaction on nonlinear models. Finally, for policy designers, it shows the need for sponsored R&D collaboration to encourage coordinated ecosystems in which sustainability goals are pursued together.

PurposeThe purpose of this paper is to analyse whether the effect of innovation subsidies on firms' R&D investment varies depending on whether the firm is suffering from financial constraints.Design/methodology/approachTo address this analysis, the authors provide a theoretical model and test their hypothesis using an econometric analysis of an unbalanced panel of 3,865 innovative Spanish firms during 2010–2017. They employ the SABI database to obtain firms' financial and economic data and incorporate firms' MORE financial rating. Specifically, the authors use the GMM-SYS technique to regress and measure the marginal effects of innovation subsidies size on firms' R&D investment and the influence of firms' financial constraints.FindingsThe results of this work indicate that financial constraints negatively moderate the effect of subsidies on R&D investment; that is, those firms that receive a subsidy and suffer financial constraints invest less in R&D projects than those which also receive the subsidy and do not suffer financial constraints. Besides, this work found that innovation subsidies alone do not significantly increase firms' R&D investment.Originality/valueFrom a neoclassical point of view, the existence of financial constraints is the justification of public innovation policies. However, due to the difficulty of measuring financial constraints, innovation literature has abandoned the analysis of this crucial variable. This work reintroduces this vital variable and analyses how it interacts with innovation subsidies on firms' R&D investment.

The University–Industry (U–I) relationship is a fundamental part of innovation systems. A wide spread of public resources has been given to promote this relationship and a large number of studies has evaluated the results. However, while innovation theory identifies this relationship as a positive instrument to increase firms’ performance, evaluation literature reports a wide range of findings. The lack of conclusiveness results in theory and evaluation literature motivates this meta-regression analysis (MRA), built on fifty-one micro-level studies published since 1995. After controlling for publication selection bias, sample, and study heterogeneities, our results show a small effect on firms’ performance. Specifically, the size of the effect is more significant for technical outcomes than economic ones. These findings have a lot of relevance for universities, firms, and policymakers for determining open-innovation strategies and public policies.

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